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Overview of Metaheuristic Algorithms
Polaris Global Journal of Scholarly Research and Trends, 2023 Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem.
Saman M. Almufti +5 more
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Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 2023 Many optimization problems are complex, challenging and take a significant amount of computational effort to solve. These problems have gained the attention of researchers and they have developed lots of metaheuristic algorithms to use for solving these ...
Aydın Sipahioğlu, İslam Altın
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The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms [PDF]
, 2020 open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms.
Caraffini, Fabio, Iacca, Giovani
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Two-Stage Eagle Strategy with Differential Evolution [PDF]
, 2012 Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications.
Deb, Suash, Yang, Xin-She
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Firefly Algorithm, Stochastic Test Functions and Design Optimisation [PDF]
, 2010 Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems.
Yang, Xin-She
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Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimize total tardiness [PDF]
, 2018 The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the
Fernández-Viagas Escudero, Víctor +2 more
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Trigonometric words ranking model for spam message classification
IET Networks, EarlyView., 2022 Abstract The significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when
Suha Mohammed Hadi +7 more
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Open Mathematics, 2017 Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a ...
Bansal Shonak +2 more
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Efficiency Analysis of Swarm Intelligence and Randomization Techniques [PDF]
, 2012 Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark.
Yang, Xin-She
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IET Networks, EarlyView., 2023 We utilise a metaheuristic optimisation method, inspired by nature, called the Lévy‐flight firefly algorithm (LFA), to tackle the power regulation and user grouping in the NOMA systems. Abstract The non‐orthogonal multiple access strategies have shown promise to boost fifth generation and sixth generation wireless networks' spectral efficiency and ...
Zaid Albataineh +4 more
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